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Feature selection for high-dimensional multi-category data using PLS-based local recursive feature elimination
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DOI：10.1016/j.eswa.2013.08.043.png)
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Abstract
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This paper focuses on high-dimensional and ultrahigh dimensional multi-category problems and presents a feature selection framework based on local recursive feature elimination (Local-RFE). Using this analytical framework, we propose a new feature selection algorithm, PLS-based local-RFE (LRFE-PLS). In order to compare the effectiveness of the proposed methodology, we also present PLS-based Global-RFE which takes all categories into consideration simultaneously. The advantage, of the proposed algorithms lies in the fact that PLS-based feature ranking can quickly delete irrelevant features and RFE can concurrently remove some redundant features. As a result, the selected feature subset is more compact. In this paper the proposed algorithms are compared to some state-of-the-art methods using multiple datasets. Experimental results show that the proposed algorithms are competitive and work effectively for high-dimensional multi-category data. Statistical tests of significance show that LRFE-PLS algorithm has better performance. The proposed algorithms can be effectively applied not only to microarray data analysis but also to image recognition and financial data analysis. (C) 2013 Elsevier Ltd. All rights reserved.
Keywords:
High-dimensional multi-category problem
Partial least squares
Recursive feature elimination
Feature selection
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